Results of study of long-fiber flax’s collection material by the parameters of economically valuable traits
Bibliographic record
Abstract
Cultivated common flax (Linum usitatissimum L.) is a traditional Russian technological crop of complex usage. Estimation of seasonal effects in economically valuable traits (productivity of straw and seeds; content of fiber and its quality) of long-fiber flax under conditions of Volga-Vyatka region (Kirov region) in 2014 - 2017 is presented in the article. Objects of study - 140 varieties of long-fiber flax different in ecological and geographic origin. Stable and plastic traits are selected according to correlation coefficient. Strong “genotype - environment” interactions were expressed by seed productivity, number of bolls and seed per plant; moderate interactions - by productivity of straw and fiber; weak interactions - by plant length, fiber content, 1000-grain mass. Strong correlation is pointed out between straw productivity and total and technical plant height (r = 0.71±0.09.. ,0.78±0.08) in average for years of study. Sources are selected for most important directions of long-fiber flax breeding: for straw productivity - Sinel, Vizit (Russia). Merilin (The Netherlands Soglasie (Belarus), Heiya 11, Heiyal2 (China); for seed productivity - Priboj, Sinichka, Er-27 (Russia), Ottawa (Canada), Soglasie (Belarus), Belinka, Merilin (The Netherlands); for fiber content- Mirazh, Peresvet, Tverskoj, Pskovsky-93, Dobrynya (Russia); Merilin (The Netherlands); Charivny, Zaryanka (Ukraine); for fiber durability - Soglasie (Belarus), Sinel, Pskovsky-93 (Russia), Kamenyar, Baltuchaj (Ukraine), B-179 (Lithuania); for fiber flexibility - Flax of Heilonjiang № 7, Huaguang 2, Heiya 11, Heiya 13 (China); Honkei-25 (Japan), Kamenyar (Ukraine), Priboj (Russia). By a complex of economically valuable traits the following genotypes are selected: Sinel, TOST 3, Peresvet (Russia), Flax of Heilonjiang № 13 and Heiya 12 (China), Merilin (The Netherlands).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".